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dc.contributor.authorCollins, Katherine M.
dc.contributor.authorCollins, James J.
dc.date.accessioned2020-04-23T14:33:46Z
dc.date.available2020-04-23T14:33:46Z
dc.date.issued2018-06
dc.identifier.issn0092-8674
dc.identifier.urihttps://hdl.handle.net/1721.1/124832
dc.description.abstractMachine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enablingone to generate models that learn from large datasets and make predictions on likely outcomes,machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research,and synthetic biology.en_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionof10.1016/j.cell.2018.05.015en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceOther repositoryen_US
dc.subjectGeneral Biochemistry, Genetics and Molecular Biologyen_US
dc.titleNext-Generation Machine Learning for Biological Networksen_US
dc.typeArticleen_US
dc.identifier.citationCamacho, Diogo M. et al. “Next-Generation Machine Learning for Biological Networks.” Cell 173 (2018) © 2018 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.relation.journalCellen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-03-05T16:16:35Z
dspace.date.submission2020-03-05T16:16:37Z
mit.journal.volume173en_US
mit.journal.issue7en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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